import os import sys import pylab as pl import validate_models reload(validate_models) import data reload(data) reps = int(sys.argv[1]) dir = str(sys.argv[2]) true_cf = data.csv2array('%s/truth_cf.csv' % (dir)) T, J = true_cf.shape validate_models.combine_output(J, T, 'bad_model', dir, reps, True) validate_models.combine_output(J, T, 'latent_simplex', dir, reps, True) validate_models.clean_up('bad_model', dir, reps) validate_models.clean_up('latent_simplex', dir, reps) os.remove('%s/truth.csv' %dir)
import os import sys import pylab as pl import validate_models reload(validate_models) import data reload(data) i = int(sys.argv[1]) dir = str(sys.argv[2]) true_std = data.csv2array('%s/truth_std.csv' % (dir)) true_cf = data.csv2array('%s/truth_cf.csv' % (dir)) std_bias = data.csv2array('%s/truth_bias.csv' % (dir))[0] validate_models.validate_once(true_cf, true_std, std_bias, True, dir, i)
import os import sys import pylab as pl import validate_models reload(validate_models) import data reload(data) reps = int(sys.argv[1]) dir = str(sys.argv[2]) true_cf = data.csv2array('%s/truth_cf.csv' % (dir)) T, J = true_cf.shape validate_models.combine_output(J, T, 'bad_model', dir, reps, True) validate_models.combine_output(J, T, 'latent_simplex', dir, reps, True) validate_models.clean_up('bad_model', dir, reps) validate_models.clean_up('latent_simplex', dir, reps) os.remove('%s/truth.csv' % dir)